Introduction

Hui Lin @Netlify

Ming Li @Amazon

2019-05-16

Schedule

Topic Time
Introduction to Data Science 08:40 - 09:10
Deep Learning 1 09:10 - 10:10
Tea Break 10:10 - 10:30
Deep Learning 2 & 3 10:30 - 12:00
Lunch 12:00 - 13:00
Big Data Cloud Platform and Hands-on 13:00 - 13:45
Deep Learning 1 Hands-on 13:45 - 14:30
Tea Break 14:30 - 14:50
Deep Learning 2 & 3 Hands-on 14:50 - 15:50
Soft Skill and Project Cycle 15:50 - 16:15
Q&A 16:15 - 16:30

Course Website

https://course2019.netlify.com/

The term no one really defined

Data science is the discipline of making data useful. Ok…so what is it?

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Three tracks of data science

(It is a group work from https://github.com/brohrer/academic_advisory/blob/master/authors.md !)

Engineering

  1. Data environment: data storage, Kafka platform, Hadoop and Spark cluster etc.

  2. Data management: parsing the logs, web scraping, API queries, and interrogating data streams.

  3. Production: integrate model and analysis into the production system

Engineering - Production

Data Pipeline

Analysis

  1. Domain knowledge

  2. Exploratory analysis

  3. Story telling

Modeling

  1. Supervised learning

  2. Unsupervised learning

  3. Customized model development

General Process of Modeling/Analytics

Three tracks of data science

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Three tracks of data science

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Three tracks of data science

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Data Science Curriculum Roadmap

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What can (should) data science do?

Data Science Hierarchy of Needs

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Data Science Types v.s Needs

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Data Science Types v.s Needs

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Data Science Types v.s Needs

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Data Science Types v.s Needs

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Types of Questions (Modeling/Analytics)

Types of Questions (Modeling/Analytics)

Where does data science belong in your organization?

A standalone team

Where does data science belong in your organization?

An embedded model

Where does data science belong in your organization?

Integrated team